Musicality enhances selective attention: spatiotemporal insights from precise separation of simultaneous neural responses

Abstract

1.Natural environments typically contain a blend of simultaneous sounds. A significant challenge in neuroscience is identifying specific neural signals corresponding to each sound and analyzing them separately. Combining frequency-tagging and machine learning, we achieved high-precision separation of neural responses to mixed melodies, classifying them by selective attention towards specific melodies. Across two magnetoencephalography datasets, individual musicality and task performance heavily influenced the attentional recruitment of cortical regions, correlating positively with top-down attention in the left parietal cortex, but negatively with bottom-up attention in the right. In prefrontal areas, neural responses indicating higher sustained selective attention reflected better performance and musicality. These results suggest musical training enhances neural mechanisms in the frontoparietal regions, boosting performance via improving top-down attention, reducing bottom-up distractions, and maintaining selective attention over time. Importantly, this work establishes the effectiveness of combining frequency-tagging with machine learning to capture cognitive and behavioral effects with stimulus precision, applicable to other studies involving simultaneous stimuli.